feat: 新增xbot agent核心代码

新增agent/app/xbot模块，包含:
- agent.py: agent核心逻辑
- config.py: 配置管理
- session.py: 会话管理
- memory.py: 记忆管理
- loop.py: 循环任务
- adapter.py: 适配器

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

agent/app/xbot/agent.py

@@ -6,9 +6,16 @@ from typing import Any, Optional
 from datetime import datetime
 
 from .loop import AgentLoop
-from .memory import MemoryConsolidator
 from .session import SessionManager
 from .adapter import XBotLLMAdapter, LLMResponse
+from . import config
+
+# 尝试导入 simplemem
+try:
+    from simplemem import SimpleMemSystem
+    HAS_SIMPLEMEM = True
+except ImportError:
+    HAS_SIMPLEMEM = False
 
 
 class SimpleToolRegistry:
@@ -84,6 +91,8 @@ class XBotAgent:
         base_url: Optional[str] = None,
         workspace: Optional[Path] = None,
         context_window_tokens: int = 200000,
+        embedding_model: Optional[str] = None,
+        embedding_base_url: Optional[str] = None,
     ):
         """
         初始化 XBot Agent
@@ -101,9 +110,15 @@ class XBotAgent:
         self.name = name
         self.role_description = role_description
 
-        # 创建工作目录
+        # 使用配置文件的默认值
+        if api_key is None:
+            api_key = config.API_KEY
+        if base_url is None:
+            base_url = config.BASE_URL
         if workspace is None:
-            workspace = Path(os.getenv("XAGENT_WORKSPACE", "./xbot_workspace"))
+            workspace = Path(config.WORKSPACE)
+
+        # 创建工作目录
         self.workspace = workspace
         self.workspace.mkdir(parents=True, exist_ok=True)
 
@@ -130,14 +145,35 @@ class XBotAgent:
         # 创建会话管理器
         self.sessions = SessionManager(self.workspace)
 
-        # 创建内存压缩器
-        self.memory = MemoryConsolidator(
-            workspace=self.workspace,
-            provider=self.provider,
-            model=model,
-            sessions=self.sessions,
-            context_window_tokens=context_window_tokens,
-        )
+        # 创建 SimpleMem 记忆系统
+        if HAS_SIMPLEMEM and api_key and config.ENABLE_SIMPLEMEM:
+            # 使用配置文件的 embedding 设置
+            emb_model = embedding_model or config.EMBEDDING_MODEL
+            emb_base = embedding_base_url or config.EMBEDDING_BASE_URL or base_url
+
+            self.memory = SimpleMemSystem(
+                api_key=api_key,
+                base_url=emb_base,
+                model=model,
+                embedding_model=emb_model,
+                db_path=str(self.workspace / "memory_db"),
+                clear_db=False,
+                # 并行处理配置
+                enable_parallel_processing=config.ENABLE_PARALLEL_PROCESSING,
+                max_parallel_workers=config.MAX_PARALLEL_WORKERS,
+                enable_parallel_retrieval=config.ENABLE_PARALLEL_RETRIEVAL,
+                max_retrieval_workers=config.MAX_RETRIEVAL_WORKERS,
+                enable_planning=config.ENABLE_PLANNING,
+                enable_reflection=config.ENABLE_REFLECTION,
+                max_reflection_rounds=config.MAX_REFLECTION_ROUNDS,
+            )
+            self._use_simplemem = True
+            print(f"SimpleMem initialized with embedding: {emb_model}, base_url: {emb_base}")
+        else:
+            self.memory = None
+            self._use_simplemem = False
+            if not api_key:
+                print("Warning: No API key provided, SimpleMem will be disabled")
 
     def _register_default_tools(self) -> None:
         """注册默认工具"""
@@ -191,6 +227,17 @@ class XBotAgent:
 
 请根据用户的问题回答，并使用 Markdown 格式输出。"""
 
+        # 如果使用 SimpleMem，检索相关记忆
+        memory_context = ""
+        if self._use_simplemem and self.memory:
+            try:
+                memory_context = self.memory.ask(user_input)
+            except Exception as e:
+                print(f"Memory retrieval error: {e}")
+
+        if memory_context:
+            system_prompt += f"\n\n相关记忆:\n{memory_context}"
+
         # 获取历史消息
         history = session.get_history(max_messages=50)
 
@@ -210,8 +257,14 @@ class XBotAgent:
             session.messages.append(m)
         self.sessions.save(session)
 
-        # 尝试内存压缩
-        await self.memory.maybe_consolidate_by_tokens(session)
+        # 保存到 SimpleMem 记忆
+        if self._use_simplemem and self.memory and final_content:
+            try:
+                self.memory.add_dialogue("User", user_input, datetime.now().isoformat())
+                self.memory.add_dialogue(self.name, final_content, datetime.now().isoformat())
+                self.memory.finalize()
+            except Exception as e:
+                print(f"Memory save error: {e}")
 
         return {
             "content": final_content or "No response",

agent/app/xbot/config.py

@@ -0,0 +1,74 @@
+"""
+XBot 配置文件
+"""
+
+# ==================== LLM 配置 ====================
+
+# 默认 LLM 提供商
+DEFAULT_PROVIDER = "openai"
+
+# 默认模型
+DEFAULT_MODEL = "gpt-4"
+
+# API Key（建议使用环境变量）
+import os
+API_KEY = os.getenv("OPENAI_API_KEY", "")
+
+# Base URL
+BASE_URL = os.getenv("OPENAI_BASE_URL", "https://api.openai.com/v1")
+
+
+# ==================== SimpleMem 记忆配置 ====================
+
+# 是否启用 SimpleMem
+ENABLE_SIMPLEMEM = True
+
+# Embedding 模型
+# 推荐: text-embedding-3-small, text-embedding-3-large, text-embedding-ada-002
+# 或使用 Qwen: Qwen/Qwen3-Embedding-0.6B
+EMBEDDING_MODEL = os.getenv("EMBEDDING_MODEL", "text-embedding-3-small")
+
+# Embedding 服务的 Base URL（可选，默认使用 BASE_URL）
+EMBEDDING_BASE_URL = os.getenv("EMBEDDING_BASE_URL", "")
+
+
+# ==================== 并行处理配置 ====================
+
+# 是否启用并行处理
+ENABLE_PARALLEL_PROCESSING = True
+MAX_PARALLEL_WORKERS = 8
+
+# 是否启用并行检索
+ENABLE_PARALLEL_RETRIEVAL = True
+MAX_RETRIEVAL_WORKERS = 4
+
+# 是否启用规划
+ENABLE_PLANNING = True
+
+# 是否启用反思
+ENABLE_REFLECTION = True
+MAX_REFLECTION_ROUNDS = 2
+
+
+# ==================== 工作目录 ====================
+
+# 工作目录（用于存储会话和记忆）
+WORKSPACE = os.getenv("XAGENT_WORKSPACE", "./xbot_workspace")
+
+# 上下文窗口大小
+CONTEXT_WINDOW_TOKENS = 200000
+
+
+# ==================== Agent 配置 ====================
+
+# 默认 Agent 配置
+DEFAULT_AGENTS = {
+    1: {
+        "name": "数据分析助手",
+        "role_description": "你是一个专业的数据分析助手，擅长分析数据、生成报告。",
+    },
+    2: {
+        "name": "代码审查助手",
+        "role_description": "你是一个专业的代码审查助手，擅长审查代码、发现bug。",
+    },
+}